Introduction to Big Data Programming Languages
The professionals dealing with big data analysis and manipulation are posed by a vital challenge over the choice of programming language that the use for this purpose. These analysts not only have to understand the problem and design the architecture, but the language plays a very important role in the program architecture’s execution and implementation.
Top 5 Big Data Programming Languages
Let us look into the features of the most popular programming languages, which have proved to be Highly Effective for analysis of big data, discussing the pros and cons with respect to data warehousing and the necessary data mining tools and structure which can be provided through these programming languages.
1. Scala
- Scala is a very popular choice of language amongst the professionals dealing with big data analysis owing to its fast and robust functionality. This is due to the fact the programming language has been designed to service a crossover between functional programming paradigm and object-oriented programming.
- The power of scalar can be proven by the very fact that two of the most popular frameworks used for processing big data i.e., Apache Spark and Apache Kafka have been used by using the primary Framework used for Scala.
- Another major reason why steel is preferred the program was is that it works on a Java-based ecosystem serving the big data, which adds to its versatility and the range of the language to which it can be used.
- On the contrary, it is comparatively less verbose than Java. (for instance, you need to write lesson 15 lines of codes in Scala which is equivalent to 100 lines for Java)
- One con that we can observe in the case of Scala is that it has a very steep learning curve which makes it very difficult for the beginners to use it efficiently.
2. Python
- Python has become one of the most versatile in a programming language that can be used over a very broad spectrum which includes big data programming as well.
- Various data analysis libraries such as SciPy, Numpy or Panda manipulation and cleaning of the frameworks related to big data are based on Python.
- Popular frameworks for deep learning/ machine learning like TensorFlow and Scikit Learn are made on the basis of Python.
- One of the most prominent drawbacks of python is that if the delivery level is slow compared to contemporary languages.
- On the other hand, the best feature of python is that can integrate with the pre-existing big data frameworks like Hadoop and spark effortlessly and allows for the performance of predictive analysis without much troubleshooting.
3. R
- R is the language of statistics that has been built upon data models and is one of the most effective languages used for or data analysis which is accurate in quantitative terms.
- Programming language comes along with a huge repository of CRAN packages or comprehensive R Archives network which helps in accomplishing the task for processing big data using the tool repository.
- Similar to python the language seamlessly integrates with Spark and Hadoop accrued with better statistical and accuracy formulation.
- The major drawback for the language it is not generic in its purpose with respect to big data analysis, meaning the courts which have been written using the language is not deployable for direct production but has to be translated into other programming languages, making it a time taking and tedious task.
4. Java
- Java even though it is an old programming language, proves to be one of the most traditionally executive all frameworks which are used for big data analysis and associated ecosystem which is used by a lot of Enterprises even today.
- The primary benefit of using Java is its stability in comparison to contemporary programming languages and the ease of use due to it being production-ready in its product/service delivery essence.
- The language has been tried and tested and has a pool of tools and libraries which can be e e used for performing various operations and monitoring the big data applications, big data software developers find Java to be a very approachable language.
- The biggest drawback of the programming language it’s lengthy verbosity. Similar function can be done in Python using 15 to 20 lines of code sums up to be around a 10- line code in Java.
- The Lambda function update which has been brought forward via JAVA 8, has to some extent reduce the velocity.
5. Go
Go is the newest edition to the programming languages which have been used for big data infrastructure and related functionalities a group of Engineers at Google who were trying to developer language which was less cumbersome than C++.
- Go powers an array of big data infrastructure and processing tools like Docker and Kubernetes, to name a few.
- Compared to its content review it is easiest to learn to induce in the development of applications, which makes it one of the best choices for budding Big Data developers.
- It is relatively easy for other programming languages to be interfaced using the ago based system, in comparison to its contemporary programming languages.
- Moreover, Enterprises have been looking at the programming languages to utilize it for developing a data analysis system due to its association with Google.
Other major languages that are used for big data analysis and have their own useful features because of which various developers use them are MATLAB, Julia and SAS.
Conclusion
Big Data Analysis is a very vast horizon that covers multiple functionalities and one has to understand the kind of task one wants to perform with the huge data set. A programmer has to identify what core values of the research that he is undertaking if it is largely statistical; R is the answer. But if he wants to use predictive modeling then Python seems a better choice.
The most important fact is to be well updated with the ongoing development (including new programming languages being designed) and be at ease with all these languages to leverage the best out of them all. Also, constant skill up-gradation and improving the ability to solve a problem and increase one’s attitude towards the complexity of big data is the best tool a developer has.
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This is a guide to Big Data Programming Languages. Here we discuss the top of the most popular programming languages, which have proved to be Highly Effective for analysis of big data. You can also go through our other related articles to learn more –
